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A Model Selection Approach To Real-Time Macroeconomic Forecasting Using Linear Models And Artificial Neural Networks

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Author Info
Norman R. Swanson
Halbert White

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Abstract

We take a model selection approach to the question of whether a class of adaptive prediction models (artificial neural networks) is useful for predicting future values of nine macroeconomic variables. We use a variety of out-of-sample forecast-based model selection criteria, including forecast error measures and forecast direction accuracy. Ex ante or real-time forecasting results based on rolling window prediction methods indicate that multivariate adaptive linear vector autoregression models often outperform a variety of (1) adaptive and nonadaptive univariate models, (2) nonadaptive multivariate models, (3) adaptive nonlinear models, and (4) professionally available survey predictions. Further, model selection based on the in-sample Schwarz information criterion apparently fails to offer a convenient shortcut to true out-of-sample performance measures. © 2000 by the President and Fellows of Harvard College and the Massachusetts Institute of Technolog

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Publisher Info
Article provided by MIT Press in its journal The Review of Economics and Statistics.

Volume (Year): 79 (1997)
Issue (Month): 4 (November)
Pages: 540-550
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Handle: RePEc:tpr:restat:v:79:y:1997:i:4:p:540-550

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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Francis X. Diebold & Glenn D. Rudebusch, 1989. "Forecasting output with the composite leading index: an ex ante analysis," Finance and Economics Discussion Series 90, Board of Governors of the Federal Reserve System (U.S.).
  2. Halbert White, 1990. "Connectionist Non-parametric Regression Multilayer Feedforward Networks Can Learn Arbitrary Mappings," University of California at San Diego, Economics Working Paper Series 90-5, Department of Economics, UC San Diego.
  3. Kurt Hornik & Maxwell Stinchcombe & Halbert White, 1990. "Universal Approximation of an Unknown Mapping And Its Derivatives Using Multilayer Feedforward Networks," University of California at San Diego, Economics Working Paper Series 89-36r, Department of Economics, UC San Diego.
  4. Dean Croushore, 1993. "Introducing: the survey of professional forecasters," Business Review, Federal Reserve Bank of Philadelphia, issue Nov, pages 3-15. [Downloadable!]
  5. Leitch, Gordon & Tanner, J Ernest, 1991. "Economic Forecast Evaluation: Profits versus the Conventional Error Measures," American Economic Review, American Economic Association, vol. 81(3), pages 580-90, June. [Downloadable!] (restricted)
  6. Fair, Ray C & Shiller, Robert J, 1990. "Comparing Information in Forecasts from Econometric Models," American Economic Review, American Economic Association, vol. 80(3), pages 375-89, June. [Downloadable!] (restricted)
  7. Stekler, H. O., 1991. "Macroeconomic forecast evaluation techniques," International Journal of Forecasting, Elsevier, vol. 7(3), pages 375-384, November. [Downloadable!] (restricted)
  8. Henriksson, Roy D & Merton, Robert C, 1981. "On Market Timing and Investment Performance. II. Statistical Procedures for Evaluating Forecasting Skills," Journal of Business, University of Chicago Press, vol. 54(4), pages 513-33, October. [Downloadable!] (restricted)
  9. Granger, Clive W J, 1993. "Strategies for Modelling Nonlinear Time-Series Relationships," The Economic Record, The Economic Society of Australia, vol. 69(206), pages 233-38, September.
  10. Meese, R. & Rogoff, K., 1988. "Was It Real? The Exchange Rate-Interest Differential Ralation Over The Modern Floating-Rate Period," Working papers 368, Wisconsin Madison - Social Systems.
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  11. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  12. Victor Zarnowitz & Phillip Braun, 1994. "Twenty-two Years of the NBER-ASA Quarterly Economic Outlook Surveys: Aspects and Comparisons of Forecasting Performance," NBER Working Papers 3965, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
  13. Keane, Michael & Runkle, David E, 1995. "Testing the Rationality of Price Forecasts: Reply," American Economic Review, American Economic Association, vol. 85(1), pages 290, March.
  14. Chung-Ming Kuan & Halbert White, 1992. "Artificial Neural Networks: An Econometric Perspective," University of California at San Diego, Economics Working Paper Series 92-11, Department of Economics, UC San Diego.
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  15. Bruce Mizrach, 1996. "Forecast Comparison in L2," Departmental Working Papers 199524, Rutgers University, Department of Economics. [Downloadable!]
  16. Sawa, Takamitsu, 1978. "Information Criteria for Discriminating among Alternative Regression Models," Econometrica, Econometric Society, vol. 46(6), pages 1273-91, November. [Downloadable!] (restricted)
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